Difference between revisions of "Publications/xu.17.icip.inc"
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=== Trained models === |
=== Trained models === |
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+ | The trained models and corresponding files for training for the proposed method on NeoBrainS12 and MRBrainS13 datasets are available in the following: |
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=== Segmentation results === |
=== Segmentation results === |
Revision as of 16:47, 6 February 2017
Method and datasets
Method
Architecture of the proposed network. We fine tune it and combine linearly fine to coarse feature maps of the pre-trained VGG network. The coarsest feature maps are discarded for the adult images.
Datasets
- Dataset of the MICCAI challenge of Neonatal Brain Segmentation 2012 (NeoBrainS12)
- Axial images acquired at 40 weeks: 2 training images + 5 test images
- Coronal images acquired at 30 weeks: 2 training images + 5 test images
- Coronal images acquired at 40 weeks: 5 test images
- Dataset of the MICCAI challenge of MR Brain Image Segmentation (MRBrainS13)
- Axial images acquired at 70 years: 5 training images + 15 test images
Materials
Trained models
The trained models and corresponding files for training for the proposed method on NeoBrainS12 and MRBrainS13 datasets are available in the following:
Segmentation results
The pre-computed segmentation results of the proposed method on NeoBrainS12 and MRBrainS13 datasets are available in the following:
- Results on Axial images at 40 weeks in NeoBrainS12 dataset are available in this archive
- Results on coronal images at 30 weeks in NeoBrainS12 dataset are available in this archive
- Results on coronal images at 40 weeks in NeoBrainS12 dataset are available in this archive
- Results on MRBrainS13 dataset are available in this archive
Illustrations
Experiments
Leave-One-Subject-Out (LOSO) cross-validation on N images + normal training/test experiments. Note that only one training image is used for LOSO 2.
LOSO experiments
Quantitative results of LOSO experiments in terms of Dice coefficient as compared to the state-of-the-art results. The last one is from P. Moeskops et al. on the 15 test images in MRBrainS13 dataset.
- Qualitative results on axial images at 40 weeks in NeoBrainS12 dataset
- Qualitative results on coronal at 30 weeks in NeoBrainS12 dataset
- Qualitative results on aging adult at 70 ages in MRBrainS13 dataset
Neonatal brain MR image segmentation
- Results on axial images at 40 weeks in NeoBrainS12 dataset. More details can be found Here
Some qualitative results
- Results on coronal images at 30 weeks in NeoBrainS12 dataset. More details can be found Here
Some qualitative results (some small errors inside the red circle)
- Results on coronal images at 40 weeks in NeoBrainS12 dataset. More details can be found Here
Some qualitative results (some small errors inside red circles)
Adult brain MR image segmentation
- Results on aging adult images at 70 years in MRBrainS13 dataset. Only top 10 methods among 38 submitted ones are shown. More results and details can be found Here
Some qualitative results